Machine Learning (ML) and especially its application to cyber-physical systems is an uprising field of research. Many approaches on how to leverage the power of ML even in small devices have been published and applied in recent years, forming the field of TinyML. While TinyML has been promising several benefits such as cost-reduction, privacy and more, studies on which of these benefits are actually crucial when deciding to apply a TinyML approach to a real-world problem have been missing so far. The author of this paper argues, that without understanding the ”why”, the community of researchers and industrial parties may not understand the reasons of applying TinyML and hence may head into research and development directions not increasing its success. This work analysis the application of TinyML approaches and the reasons behind in recent years for three important fields of application: consumer electronics, manufacturing and automotive. It determines the distribution of TinyML applications in the named field and examines which of the bespoken benefits of TinyML were actually driving the decision to use it. Furthermore, this work investigates in cross connections between the benefits and hence points out the main combinations of benefits to the adopter.
Electrocardiography (ECG) is a promising approach for continuous fetal heart rate monitoring. Its morphology can provide information on fetal health to guide patient care by clinicians. However, fetal ECGs extracted from abdominal ECGs are often too weak to reliably detect fetal heart rate. This study evaluates the application of a U-Net architecture for accurate R-peak detection in low-SNR fetal ECG signals. The proposed method achieves high accuracy with a positive predictive value of 99.81%, sensitivity of 100.00%, and an F1-score of 99.91% on direct fetal ECG from the Abdominal and Direct ECG Database, with significantly reduced false predictions, and outperforming two other baseline methods compared with. Notably, our approach demonstrates robustness, accurately predicting peaks in regions of high distortion, a capability unmatched by other methods evaluated. This finding indicates the suitability and benefits of the U-Net architecture for peak detection in fetal ECG signals.
This paper presents axiomatic proof rules for exception handling and argues that exceptions are no more than forward goto statements. The rules are defined within Hoare’s native proof system with Dijkstra’s weakest precondition model as a mathematical foundation.
The biomedical study of large-scale multimodal neuroimaging data is popular nowadays due to advancements in deep learning and machine learning algorithms. This article has studied four different types of neurocircuitry disorders or diseases that help human beings prevent or detect them in the early stages for easily starting the treatment. a) Early detection and classification of Alzheimer's disease (AD) b) Parkinson's detection (a neurocircuitry disorder that had slowed the activity) c) Generalized anxiety disorder d) stress prediction for patients of stress) Here, this work briefly reviews the important literature on neurological diseases and explores how deep learning can help researchers to diagnose the disease at its primary stages
The fast and precise screening of suspected instances is limited due to a lack of resources and tight test environment requirements. In rare circumstances of RT-PCR inspection, erroneous negative results are also encountered. Detection of COVID-19 now requires at least one day to produce a result. Using an X-ray image, this method will provide quick and accurate findings. In this project using CNN algorithm the system recognises COVID-19. Two different publicly available datasets were used in this project. DL-based models provide a precise and well organised system for detecting it, resulting in a considerable increase of accuracy in image processing. This model has ac
